Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute

戴伦·帕特尔——深入探讨扩大人工智能计算的三大瓶颈

Dwarkesh Podcast

2026-03-14

2 小时 30 分钟
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Dylan Patel, founder of SemiAnalysis, provides a deep dive into the 3 big bottlenecks to scaling AI compute: logic, memory, and power. And walks through the economics of labs, hyperscalers, foundries, and fab equipment manufacturers. Learned a ton about every single level of the stack. Enjoy! Watch on YouTube; read the transcript. Sponsors * Mercury has already saved me a bunch of time this tax season. Last year, I used Mercury to request W-9s from all the contractors I worked with. Then, when it came time to issue 1099s this year, I literally just clicked a button and Mercury sent them out. Learn more at mercury.com. * Labelbox noticed that even when voice models appear to take interruptions in stride, their performance degrades. To figure out why, they built a new evaluation pipeline called EchoChain. EchoChain diagnoses voice models’ specific failure modes, letting you understand what your model needs to truly handle interruptions. Check it out at labelbox.com/dwarkesh. * Jane Street is basically a research lab with a trading desk attached – and their infrastructure backs this up. They’ve got tens of thousands of GPUs, hundreds of thousands of CPU cores, and exabytes of storage. This is what it takes to find subtle signals hidden deep within noisy market data. If this sounds interesting, you can explore open positions at janestreet.com/dwarkesh. Timestamps (00:00:00) – Why an H100 is worth more today than 3 years ago (00:24:52) – Nvidia secured TSMC allocation early; Google is getting squeezed (00:34:34) – ASML will be the #1 constraint for AI compute scaling by 2030 (00:55:47) – Can't we just use TSMC's older fabs? (01:05:37) – When will China outscale the West in semis? (01:16:01) – The enormous incoming memory crunch (01:42:34) – Scaling power in the US will not be a problem (01:54:44) – Space GPUs aren't happening this decade (02:14:07) – Why aren't more hedge funds making the AGI trade? (02:18:30) – Will TSMC kick Apple out from N2? (02:24:16) – Robots and Taiwan risk Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe
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  • All right, this is the episode of My Roommate Teaches Me Semiconductors.

  • It's also the send-off for this current set.

  • Yeah, you know, after you use it, I'm like, I can't use this again.

  • I gotta get out of here.

  • No sloppy seconds for DoorCash.

  • Okay, Dylan is the CEO of SemiAnalysis.

  • Dylan, the burning question I have for you, if you add up the big four,

  • Amazon, Meta, Google, Microsoft, they're combined...

  • forecasted capex that you published recently this year is 600 billion dollars and given uh you know yearly prices of renting that compute that would be like close to 50 gigawatts now obviously we're not putting on 50 gigawatts this year so presumably that's paying

  • for compute that is going to be coming online over the coming years so i have a question about what how to think about the timeline around when that capex comes online similar question for the labs where you know OpenAI just announced that they raised $110 billion.

  • Anthropic just announced they raised $30 billion.

  • And if you look at the compute that they have coming online this year,

  • you should tell me how much it is,

  • but like, isn't it another four gigawatts total that they'll have this year?

  • It feels like the cost to rent the compute that OpenAI and Anthropic will have this year to like sustain their compute spend at,

  • you know, $10, $13 billion a gigawatt.

  • Those individual raises alone are like enough to cover their compute spend for the year.

  • And then this is not even including the revenue that they're going to earn this year.

  • So help me understand first,

  • when is the timescale at which the big tech CapEx is actually coming online?